%0 Conference Proceedings
%T Management of a Production Cell Lubrication System with Model Predictive Control
%+ Institute of Industrial Technology and Automation - National Research Council
%+ Politecnico di Milano [Milan] (POLIMI)
%A Cataldo, Andrea
%A Perizzato, Andrea
%A Scattolini, Riccardo
%Z Part 1: Knowledge-Based Performance Improvement
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B IFIP International Conference on Advances in Production Management Systems (APMS)
%C Ajaccio, France
%Y Bernard Grabot
%Y Bruno Vallespir
%Y Samuel Gomes
%Y Abdelaziz Bouras
%Y Dimitris Kiritsis
%I Springer
%3 Advances in Production Management Systems. Innovative and Knowledge-Based Production Management in a Global-Local World
%V AICT-440
%N Part III
%P 131-138
%8 2014-09-20
%D 2014
%R 10.1007/978-3-662-44733-8_17
%K Model based control
%K Model predictive control
%K Hybrid optimal control
%K Production plant energy efficiency
%K Plant energy optimization
%Z Computer Science [cs]Conference papers
%X The energy efficiency of manufacturing systems represents a topic of huge interest for the management of innovative production plants. In this paper, a production cell based on three operating machines has been taken into account. In particular, each machine has an independent lubrication system whose lubricant is cooled by a centralized cooling system, while the lubrication fluid temperatures must be maintained inside known upper and lower bounds, and the controller of the centralized cooling system has to minimize the cooling power. In order to control the lubrication and cooling processes, a Model Predictive Controller (MPC) has been designed, synthetized, implemented and simulated.The main advantage of the proposed algorithm consists in the possibility to directly consider the temperature limits together with the maximum bound of the cooling power directly into the optimization problem. This means that the control action is computed using the a-priori knowledge of these bounds, resulting in better temperature profiles then those obtained with standard controllers, e.g. with saturated Proportional, Integral, Derivative (PID) ones.
%G English
%Z TC 5
%Z WG 5.7
%2 https://inria.hal.science/hal-01387157/document
%2 https://inria.hal.science/hal-01387157/file/978-3-662-44733-8_17_Chapter.pdf
%L hal-01387157
%U https://inria.hal.science/hal-01387157
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-APMS
%~ IFIP-WG5-7
%~ IFIP-AICT-440